Overview

Brought to you by YData

Dataset statistics

Number of variables11
Number of observations61871
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory28.4 MiB
Average record size in memory481.8 B

Variable types

Numeric5
Text6

Alerts

Billing Year is highly overall correlated with YearHigh correlation
Year is highly overall correlated with Billing YearHigh correlation
Year is highly skewed (γ1 = -27.21613897) Skewed
Billing Year is highly skewed (γ1 = -26.12502358) Skewed
Index is uniformly distributed Uniform
Index has unique values Unique

Reproduction

Analysis started2025-04-07 12:32:34.751767
Analysis finished2025-04-07 12:32:39.613680
Duration4.86 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Index
Real number (ℝ)

Uniform  Unique 

Distinct61871
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30936
Minimum1
Maximum61871
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.5 KiB
2025-04-07T18:02:39.671413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3094.5
Q115468.5
median30936
Q346403.5
95-th percentile58777.5
Maximum61871
Range61870
Interquartile range (IQR)30935

Descriptive statistics

Standard deviation17860.764
Coefficient of variation (CV)0.5773456
Kurtosis-1.2
Mean30936
Median Absolute Deviation (MAD)15468
Skewness0
Sum1.9140413 × 109
Variance3.1900688 × 108
MonotonicityStrictly increasing
2025-04-07T18:02:39.770150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61871 1
 
< 0.1%
1 1
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
Other values (61861) 61861
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
61871 1
< 0.1%
61870 1
< 0.1%
61869 1
< 0.1%
61868 1
< 0.1%
61867 1
< 0.1%
61866 1
< 0.1%
61865 1
< 0.1%
61864 1
< 0.1%
61863 1
< 0.1%
61862 1
< 0.1%

Pin#
Text

Distinct60722
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Memory size4.7 MiB
2025-04-07T18:02:39.936197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length28
Median length22
Mean length22.01474
Min length22

Characters and Unicode

Total characters1362074
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique59815 ?
Unique (%)96.7%

Sample

1st row01-04-19-0-000-001.000
2nd row01-04-19-0-000-002.000
3rd row01-04-19-0-000-003.000
4th row01-04-19-0-000-004.000
5th row01-04-19-0-000-005.000
ValueCountFrequency (%)
20-07-35-0-000-047.001 10
 
< 0.1%
15-05-21-2-001-002.000 10
 
< 0.1%
15-05-21-1-000-063.013 10
 
< 0.1%
15-05-21-1-000-063.011 10
 
< 0.1%
15-05-21-1-000-063.012 10
 
< 0.1%
15-05-21-1-000-063.010 10
 
< 0.1%
20-07-35-0-000-047.005 10
 
< 0.1%
20-07-35-0-000-044.000 10
 
< 0.1%
20-07-35-0-000-045.000 10
 
< 0.1%
20-07-35-0-000-046.000 10
 
< 0.1%
Other values (60712) 61771
99.8%
2025-04-07T18:02:40.151281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 544418
40.0%
- 309355
22.7%
1 129246
 
9.5%
2 75846
 
5.6%
. 62194
 
4.6%
3 57320
 
4.2%
4 38528
 
2.8%
5 37413
 
2.7%
6 33074
 
2.4%
9 31525
 
2.3%
Other values (4) 43155
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 990523
72.7%
Dash Punctuation 309355
 
22.7%
Other Punctuation 62194
 
4.6%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 544418
55.0%
1 129246
 
13.0%
2 75846
 
7.7%
3 57320
 
5.8%
4 38528
 
3.9%
5 37413
 
3.8%
6 33074
 
3.3%
9 31525
 
3.2%
8 21616
 
2.2%
7 21537
 
2.2%
Uppercase Letter
ValueCountFrequency (%)
W 1
50.0%
I 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 309355
100.0%
Other Punctuation
ValueCountFrequency (%)
. 62194
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1362072
> 99.9%
Latin 2
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 544418
40.0%
- 309355
22.7%
1 129246
 
9.5%
2 75846
 
5.6%
. 62194
 
4.6%
3 57320
 
4.2%
4 38528
 
2.8%
5 37413
 
2.7%
6 33074
 
2.4%
9 31525
 
2.3%
Other values (2) 43153
 
3.2%
Latin
ValueCountFrequency (%)
W 1
50.0%
I 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1362074
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 544418
40.0%
- 309355
22.7%
1 129246
 
9.5%
2 75846
 
5.6%
. 62194
 
4.6%
3 57320
 
4.2%
4 38528
 
2.8%
5 37413
 
2.7%
6 33074
 
2.4%
9 31525
 
2.3%
Other values (4) 43155
 
3.2%

Description
Text

Distinct43910
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Memory size4.9 MiB
2025-04-07T18:02:40.334616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length255
Median length228
Mean length26.857461
Min length5

Characters and Unicode

Total characters1661698
Distinct characters72
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35776 ?
Unique (%)57.8%

Sample

1st rowCHANDLER, TOMMY OLEN & SHERRY
2nd rowSUMMERLIN, LORRAINE D
3rd rowDUNCAN ZACKARY TYLER AND DUNCAN TIFFANY DAWN
4th rowPARKER, NELSON E & PAIGE L
5th rowPUTMAN, BARBARA K
ValueCountFrequency (%)
19977
 
6.9%
and 13743
 
4.7%
llc 5098
 
1.8%
l 3675
 
1.3%
a 3197
 
1.1%
d 2582
 
0.9%
m 2576
 
0.9%
j 2460
 
0.8%
r 2225
 
0.8%
estate 2220
 
0.8%
Other values (16465) 232811
80.1%
2025-04-07T18:02:40.617860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
228693
13.8%
A 150786
 
9.1%
E 150029
 
9.0%
N 114771
 
6.9%
R 114233
 
6.9%
L 98805
 
5.9%
I 85032
 
5.1%
S 73593
 
4.4%
T 73089
 
4.4%
O 72916
 
4.4%
Other values (62) 499751
30.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1369444
82.4%
Space Separator 228693
 
13.8%
Other Punctuation 56553
 
3.4%
Open Punctuation 2265
 
0.1%
Close Punctuation 2245
 
0.1%
Decimal Number 1481
 
0.1%
Dash Punctuation 905
 
0.1%
Lowercase Letter 109
 
< 0.1%
Modifier Symbol 3
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 150786
11.0%
E 150029
11.0%
N 114771
 
8.4%
R 114233
 
8.3%
L 98805
 
7.2%
I 85032
 
6.2%
S 73593
 
5.4%
T 73089
 
5.3%
O 72916
 
5.3%
D 61283
 
4.5%
Other values (16) 374907
27.4%
Lowercase Letter
ValueCountFrequency (%)
e 15
13.8%
n 11
10.1%
i 10
9.2%
t 8
 
7.3%
a 8
 
7.3%
l 8
 
7.3%
o 7
 
6.4%
r 7
 
6.4%
y 7
 
6.4%
c 7
 
6.4%
Other values (8) 21
19.3%
Other Punctuation
ValueCountFrequency (%)
, 35860
63.4%
& 19886
35.2%
/ 405
 
0.7%
' 244
 
0.4%
. 58
 
0.1%
" 26
 
< 0.1%
% 23
 
< 0.1%
* 14
 
< 0.1%
# 13
 
< 0.1%
; 13
 
< 0.1%
Other values (3) 11
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1 488
33.0%
2 319
21.5%
4 158
 
10.7%
0 144
 
9.7%
3 139
 
9.4%
5 81
 
5.5%
8 57
 
3.8%
6 38
 
2.6%
9 31
 
2.1%
7 26
 
1.8%
Space Separator
ValueCountFrequency (%)
228693
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2265
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2245
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 905
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1369553
82.4%
Common 292145
 
17.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 150786
11.0%
E 150029
11.0%
N 114771
 
8.4%
R 114233
 
8.3%
L 98805
 
7.2%
I 85032
 
6.2%
S 73593
 
5.4%
T 73089
 
5.3%
O 72916
 
5.3%
D 61283
 
4.5%
Other values (34) 375016
27.4%
Common
ValueCountFrequency (%)
228693
78.3%
, 35860
 
12.3%
& 19886
 
6.8%
( 2265
 
0.8%
) 2245
 
0.8%
- 905
 
0.3%
1 488
 
0.2%
/ 405
 
0.1%
2 319
 
0.1%
' 244
 
0.1%
Other values (18) 835
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1661698
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
228693
13.8%
A 150786
 
9.1%
E 150029
 
9.0%
N 114771
 
6.9%
R 114233
 
6.9%
L 98805
 
5.9%
I 85032
 
5.1%
S 73593
 
4.4%
T 73089
 
4.4%
O 72916
 
4.4%
Other values (62) 499751
30.1%

Description1
Text

Distinct43139
Distinct (%)69.7%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
2025-04-07T18:02:40.836047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length66
Median length59
Mean length17.978245
Min length1

Characters and Unicode

Total characters1112332
Distinct characters47
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39206 ?
Unique (%)63.4%

Sample

1st row0
2nd row1106 THOMAS LANE
3rd row831 DUNCAN FARM RD
4th row0
5th row0
ValueCountFrequency (%)
road 25056
 
11.9%
0 16827
 
8.0%
drive 8993
 
4.3%
street 7525
 
3.6%
avenue 4531
 
2.1%
highway 4084
 
1.9%
circle 3346
 
1.6%
lane 3333
 
1.6%
alabama 2232
 
1.1%
s 1629
 
0.8%
Other values (9274) 133518
63.3%
2025-04-07T18:02:41.167794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
149203
 
13.4%
E 89898
 
8.1%
A 84828
 
7.6%
R 82213
 
7.4%
O 62949
 
5.7%
D 49942
 
4.5%
I 48117
 
4.3%
N 47214
 
4.2%
L 44848
 
4.0%
T 43820
 
3.9%
Other values (37) 409300
36.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 780853
70.2%
Decimal Number 178901
 
16.1%
Space Separator 149203
 
13.4%
Other Punctuation 2087
 
0.2%
Dash Punctuation 1284
 
0.1%
Open Punctuation 2
 
< 0.1%
Close Punctuation 2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 89898
11.5%
A 84828
10.9%
R 82213
10.5%
O 62949
 
8.1%
D 49942
 
6.4%
I 48117
 
6.2%
N 47214
 
6.0%
L 44848
 
5.7%
T 43820
 
5.6%
S 34261
 
4.4%
Other values (16) 192763
24.7%
Decimal Number
ValueCountFrequency (%)
0 37384
20.9%
1 30683
17.2%
2 18902
10.6%
5 16822
9.4%
3 16634
9.3%
4 14695
 
8.2%
6 12052
 
6.7%
7 11397
 
6.4%
9 10274
 
5.7%
8 10058
 
5.6%
Other Punctuation
ValueCountFrequency (%)
, 1922
92.1%
# 115
 
5.5%
/ 19
 
0.9%
' 17
 
0.8%
. 8
 
0.4%
& 5
 
0.2%
; 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
149203
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1284
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 780853
70.2%
Common 331479
29.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 89898
11.5%
A 84828
10.9%
R 82213
10.5%
O 62949
 
8.1%
D 49942
 
6.4%
I 48117
 
6.2%
N 47214
 
6.0%
L 44848
 
5.7%
T 43820
 
5.6%
S 34261
 
4.4%
Other values (16) 192763
24.7%
Common
ValueCountFrequency (%)
149203
45.0%
0 37384
 
11.3%
1 30683
 
9.3%
2 18902
 
5.7%
5 16822
 
5.1%
3 16634
 
5.0%
4 14695
 
4.4%
6 12052
 
3.6%
7 11397
 
3.4%
9 10274
 
3.1%
Other values (11) 13433
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1112332
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
149203
 
13.4%
E 89898
 
8.1%
A 84828
 
7.6%
R 82213
 
7.4%
O 62949
 
5.7%
D 49942
 
4.5%
I 48117
 
4.3%
N 47214
 
4.2%
L 44848
 
4.0%
T 43820
 
3.9%
Other values (37) 409300
36.8%

Account
Real number (ℝ)

Distinct55388
Distinct (%)89.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean228981.09
Minimum8
Maximum530156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.5 KiB
2025-04-07T18:02:41.251244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile12702.5
Q178893.5
median147859
Q3506649.5
95-th percentile520763.5
Maximum530156
Range530148
Interquartile range (IQR)427756

Descriptive statistics

Standard deviation197122.66
Coefficient of variation (CV)0.86086872
Kurtosis-1.3579252
Mean228981.09
Median Absolute Deviation (MAD)94054
Skewness0.6351415
Sum1.4167289 × 1010
Variance3.8857343 × 1010
MonotonicityNot monotonic
2025-04-07T18:02:41.351213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
507750 88
 
0.1%
100632 66
 
0.1%
518407 62
 
0.1%
518098 49
 
0.1%
14207 47
 
0.1%
508049 45
 
0.1%
514293 31
 
0.1%
519839 31
 
0.1%
64797 28
 
< 0.1%
520367 24
 
< 0.1%
Other values (55378) 61400
99.2%
ValueCountFrequency (%)
8 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
12 1
< 0.1%
13 1
< 0.1%
14 1
< 0.1%
15 1
< 0.1%
16 1
< 0.1%
17 1
< 0.1%
18 1
< 0.1%
ValueCountFrequency (%)
530156 1
< 0.1%
530064 1
< 0.1%
529541 1
< 0.1%
529540 1
< 0.1%
529359 1
< 0.1%
529216 1
< 0.1%
529215 1
< 0.1%
529135 1
< 0.1%
529105 1
< 0.1%
528882 1
< 0.1%

Parcel
Text

Distinct60722
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Memory size4.7 MiB
2025-04-07T18:02:41.503211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length28
Median length22
Mean length22.01474
Min length22

Characters and Unicode

Total characters1362074
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique59815 ?
Unique (%)96.7%

Sample

1st row01-04-19-0-000-001.000
2nd row01-04-19-0-000-002.000
3rd row01-04-19-0-000-003.000
4th row01-04-19-0-000-004.000
5th row01-04-19-0-000-005.000
ValueCountFrequency (%)
20-07-35-0-000-047.001 10
 
< 0.1%
15-05-21-2-001-002.000 10
 
< 0.1%
15-05-21-1-000-063.013 10
 
< 0.1%
15-05-21-1-000-063.011 10
 
< 0.1%
15-05-21-1-000-063.012 10
 
< 0.1%
15-05-21-1-000-063.010 10
 
< 0.1%
20-07-35-0-000-047.005 10
 
< 0.1%
20-07-35-0-000-044.000 10
 
< 0.1%
20-07-35-0-000-045.000 10
 
< 0.1%
20-07-35-0-000-046.000 10
 
< 0.1%
Other values (60712) 61771
99.8%
2025-04-07T18:02:41.720327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 544418
40.0%
- 309355
22.7%
1 129246
 
9.5%
2 75846
 
5.6%
. 62194
 
4.6%
3 57320
 
4.2%
4 38528
 
2.8%
5 37413
 
2.7%
6 33074
 
2.4%
9 31525
 
2.3%
Other values (4) 43155
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 990523
72.7%
Dash Punctuation 309355
 
22.7%
Other Punctuation 62194
 
4.6%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 544418
55.0%
1 129246
 
13.0%
2 75846
 
7.7%
3 57320
 
5.8%
4 38528
 
3.9%
5 37413
 
3.8%
6 33074
 
3.3%
9 31525
 
3.2%
8 21616
 
2.2%
7 21537
 
2.2%
Uppercase Letter
ValueCountFrequency (%)
W 1
50.0%
I 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 309355
100.0%
Other Punctuation
ValueCountFrequency (%)
. 62194
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1362072
> 99.9%
Latin 2
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 544418
40.0%
- 309355
22.7%
1 129246
 
9.5%
2 75846
 
5.6%
. 62194
 
4.6%
3 57320
 
4.2%
4 38528
 
2.8%
5 37413
 
2.7%
6 33074
 
2.4%
9 31525
 
2.3%
Other values (2) 43153
 
3.2%
Latin
ValueCountFrequency (%)
W 1
50.0%
I 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1362074
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 544418
40.0%
- 309355
22.7%
1 129246
 
9.5%
2 75846
 
5.6%
. 62194
 
4.6%
3 57320
 
4.2%
4 38528
 
2.8%
5 37413
 
2.7%
6 33074
 
2.4%
9 31525
 
2.3%
Other values (4) 43155
 
3.2%

Year
Real number (ℝ)

High correlation  Skewed 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2023.9909
Minimum2016
Maximum2025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.5 KiB
2025-04-07T18:02:41.784495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2024
Q12024
median2024
Q32024
95-th percentile2024
Maximum2025
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.23066374
Coefficient of variation (CV)0.00011396481
Kurtosis794.77406
Mean2023.9909
Median Absolute Deviation (MAD)0
Skewness-27.216139
Sum1.2522634 × 108
Variance0.053205762
MonotonicityNot monotonic
2025-04-07T18:02:41.850033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2024 61721
99.8%
2025 18
 
< 0.1%
2023 18
 
< 0.1%
2022 17
 
< 0.1%
2021 17
 
< 0.1%
2020 16
 
< 0.1%
2019 16
 
< 0.1%
2018 16
 
< 0.1%
2017 16
 
< 0.1%
2016 16
 
< 0.1%
ValueCountFrequency (%)
2016 16
 
< 0.1%
2017 16
 
< 0.1%
2018 16
 
< 0.1%
2019 16
 
< 0.1%
2020 16
 
< 0.1%
2021 17
 
< 0.1%
2022 17
 
< 0.1%
2023 18
 
< 0.1%
2024 61721
99.8%
2025 18
 
< 0.1%
ValueCountFrequency (%)
2025 18
 
< 0.1%
2024 61721
99.8%
2023 18
 
< 0.1%
2022 17
 
< 0.1%
2021 17
 
< 0.1%
2020 16
 
< 0.1%
2019 16
 
< 0.1%
2018 16
 
< 0.1%
2017 16
 
< 0.1%
2016 16
 
< 0.1%

Billing Year
Real number (ℝ)

High correlation  Skewed 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2023.9899
Minimum2016
Maximum2025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.5 KiB
2025-04-07T18:02:41.901176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2024
Q12024
median2024
Q32024
95-th percentile2024
Maximum2025
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.23871546
Coefficient of variation (CV)0.00011794301
Kurtosis735.78907
Mean2023.9899
Median Absolute Deviation (MAD)0
Skewness-26.125024
Sum1.2522628 × 108
Variance0.056985069
MonotonicityNot monotonic
2025-04-07T18:02:41.967835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2024 61691
99.7%
2023 38
 
0.1%
2022 21
 
< 0.1%
2025 18
 
< 0.1%
2021 18
 
< 0.1%
2020 17
 
< 0.1%
2019 17
 
< 0.1%
2018 17
 
< 0.1%
2017 17
 
< 0.1%
2016 17
 
< 0.1%
ValueCountFrequency (%)
2016 17
 
< 0.1%
2017 17
 
< 0.1%
2018 17
 
< 0.1%
2019 17
 
< 0.1%
2020 17
 
< 0.1%
2021 18
 
< 0.1%
2022 21
 
< 0.1%
2023 38
 
0.1%
2024 61691
99.7%
2025 18
 
< 0.1%
ValueCountFrequency (%)
2025 18
 
< 0.1%
2024 61691
99.7%
2023 38
 
0.1%
2022 21
 
< 0.1%
2021 18
 
< 0.1%
2020 17
 
< 0.1%
2019 17
 
< 0.1%
2018 17
 
< 0.1%
2017 17
 
< 0.1%
2016 17
 
< 0.1%

Pin
Real number (ℝ)

Distinct61687
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35698.201
Minimum1
Maximum84930
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.5 KiB
2025-04-07T18:02:42.051226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3365.5
Q116649.5
median33152
Q353837.5
95-th percentile75940.5
Maximum84930
Range84929
Interquartile range (IQR)37188

Descriptive statistics

Standard deviation22850.122
Coefficient of variation (CV)0.64009169
Kurtosis-0.88640256
Mean35698.201
Median Absolute Deviation (MAD)18001
Skewness0.37133171
Sum2.2086834 × 109
Variance5.2212808 × 108
MonotonicityNot monotonic
2025-04-07T18:02:42.153542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23015 10
 
< 0.1%
43135 10
 
< 0.1%
43134 10
 
< 0.1%
43133 10
 
< 0.1%
23016 10
 
< 0.1%
23017 10
 
< 0.1%
23012 10
 
< 0.1%
23013 10
 
< 0.1%
43132 10
 
< 0.1%
43126 10
 
< 0.1%
Other values (61677) 61771
99.8%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
84930 1
< 0.1%
84884 1
< 0.1%
84852 1
< 0.1%
84851 1
< 0.1%
84850 1
< 0.1%
84845 1
< 0.1%
84844 1
< 0.1%
84688 1
< 0.1%
84470 1
< 0.1%
84466 1
< 0.1%

Total Tax
Text

Distinct21773
Distinct (%)35.2%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
2025-04-07T18:02:42.401155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length10
Mean length6.931939
Min length5

Characters and Unicode

Total characters428886
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13944 ?
Unique (%)22.5%

Sample

1st row$131.75
2nd row$135.75
3rd row$157.50
4th row$483.75
5th row$95.25
ValueCountFrequency (%)
0.00 9239
 
14.9%
71.25 117
 
0.2%
132.00 108
 
0.2%
43.50 101
 
0.2%
83.25 99
 
0.2%
175.50 94
 
0.2%
258.00 93
 
0.2%
37.50 83
 
0.1%
16.50 82
 
0.1%
173.34 80
 
0.1%
Other values (21763) 51775
83.7%
2025-04-07T18:02:42.717734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 73426
17.1%
$ 61871
14.4%
. 61871
14.4%
5 38337
8.9%
1 30701
7.2%
2 29883
7.0%
7 23235
 
5.4%
4 21136
 
4.9%
3 21096
 
4.9%
6 19751
 
4.6%
Other values (3) 47579
11.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 292293
68.2%
Other Punctuation 74722
 
17.4%
Currency Symbol 61871
 
14.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 73426
25.1%
5 38337
13.1%
1 30701
10.5%
2 29883
10.2%
7 23235
 
7.9%
4 21136
 
7.2%
3 21096
 
7.2%
6 19751
 
6.8%
8 18256
 
6.2%
9 16472
 
5.6%
Other Punctuation
ValueCountFrequency (%)
. 61871
82.8%
, 12851
 
17.2%
Currency Symbol
ValueCountFrequency (%)
$ 61871
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 428886
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 73426
17.1%
$ 61871
14.4%
. 61871
14.4%
5 38337
8.9%
1 30701
7.2%
2 29883
7.0%
7 23235
 
5.4%
4 21136
 
4.9%
3 21096
 
4.9%
6 19751
 
4.6%
Other values (3) 47579
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 428886
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 73426
17.1%
$ 61871
14.4%
. 61871
14.4%
5 38337
8.9%
1 30701
7.2%
2 29883
7.0%
7 23235
 
5.4%
4 21136
 
4.9%
3 21096
 
4.9%
6 19751
 
4.6%
Other values (3) 47579
11.1%

Balance Due
Text

Distinct582
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size3.6 MiB
2025-04-07T18:02:42.937788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length4
Mean length4.0346527
Min length4

Characters and Unicode

Total characters249628
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique539 ?
Unique (%)0.9%

Sample

1st rowPAID
2nd rowPAID
3rd rowPAID
4th rowPAID
5th rowPAID
ValueCountFrequency (%)
paid 51984
84.0%
none 9239
 
14.9%
1,624.92 8
 
< 0.1%
775.24 7
 
< 0.1%
965.75 5
 
< 0.1%
219.64 5
 
< 0.1%
53.22 4
 
< 0.1%
262.55 4
 
< 0.1%
1,030.14 3
 
< 0.1%
92.22 3
 
< 0.1%
Other values (572) 609
 
1.0%
2025-04-07T18:02:43.236103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
P 51984
20.8%
A 51984
20.8%
I 51984
20.8%
D 51984
20.8%
N 18478
 
7.4%
O 9239
 
3.7%
E 9239
 
3.7%
$ 648
 
0.3%
. 648
 
0.3%
1 427
 
0.2%
Other values (10) 3013
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 244892
98.1%
Decimal Number 3281
 
1.3%
Other Punctuation 807
 
0.3%
Currency Symbol 648
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 427
13.0%
2 408
12.4%
6 347
10.6%
3 346
10.5%
4 338
10.3%
0 307
9.4%
5 298
9.1%
7 295
9.0%
8 272
8.3%
9 243
7.4%
Uppercase Letter
ValueCountFrequency (%)
P 51984
21.2%
A 51984
21.2%
I 51984
21.2%
D 51984
21.2%
N 18478
 
7.5%
O 9239
 
3.8%
E 9239
 
3.8%
Other Punctuation
ValueCountFrequency (%)
. 648
80.3%
, 159
 
19.7%
Currency Symbol
ValueCountFrequency (%)
$ 648
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 244892
98.1%
Common 4736
 
1.9%

Most frequent character per script

Common
ValueCountFrequency (%)
$ 648
13.7%
. 648
13.7%
1 427
9.0%
2 408
8.6%
6 347
7.3%
3 346
7.3%
4 338
7.1%
0 307
6.5%
5 298
6.3%
7 295
6.2%
Other values (3) 674
14.2%
Latin
ValueCountFrequency (%)
P 51984
21.2%
A 51984
21.2%
I 51984
21.2%
D 51984
21.2%
N 18478
 
7.5%
O 9239
 
3.8%
E 9239
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 249628
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 51984
20.8%
A 51984
20.8%
I 51984
20.8%
D 51984
20.8%
N 18478
 
7.4%
O 9239
 
3.7%
E 9239
 
3.7%
$ 648
 
0.3%
. 648
 
0.3%
1 427
 
0.2%
Other values (10) 3013
 
1.2%

Interactions

2025-04-07T18:02:38.920183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T18:02:37.337162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T18:02:37.757316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T18:02:38.154186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T18:02:38.534772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T18:02:39.004460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T18:02:37.418260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T18:02:37.834867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T18:02:38.234095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T18:02:38.618095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T18:02:39.084778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T18:02:37.501501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T18:02:37.918388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T18:02:38.302503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T18:02:38.702540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T18:02:39.153562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T18:02:37.584774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T18:02:37.984799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T18:02:38.387246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T18:02:38.768016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T18:02:39.234258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T18:02:37.668124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T18:02:38.070379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T18:02:38.451576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-07T18:02:38.849810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-04-07T18:02:43.284318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AccountBilling YearIndexPinYear
Account1.0000.0140.0160.1960.018
Billing Year0.0141.000-0.037-0.0070.913
Index0.016-0.0371.0000.480-0.032
Pin0.196-0.0070.4801.000-0.006
Year0.0180.913-0.032-0.0061.000

Missing values

2025-04-07T18:02:39.351323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-07T18:02:39.468189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IndexPin#DescriptionDescription1AccountParcelYearBilling YearPinTotal TaxBalance Due
0101-04-19-0-000-001.000CHANDLER, TOMMY OLEN & SHERRY08390901-04-19-0-000-001.000202420241$131.75PAID
1201-04-19-0-000-002.000SUMMERLIN, LORRAINE D1106 THOMAS LANE16081501-04-19-0-000-002.000202420242$135.75PAID
2301-04-19-0-000-003.000DUNCAN ZACKARY TYLER AND DUNCAN TIFFANY DAWN831 DUNCAN FARM RD51332301-04-19-0-000-003.000202420243$157.50PAID
3401-04-19-0-000-004.000PARKER, NELSON E & PAIGE L015909001-04-19-0-000-004.000202420244$483.75PAID
4501-04-19-0-000-005.000PUTMAN, BARBARA K017394701-04-19-0-000-005.000202420245$95.25PAID
5601-04-19-0-000-006.000DUNCAN ZACKARY TYLER AND DUNCAN TIFFANY DAWN0 THOMAS LANE51332301-04-19-0-000-006.000202420246$38.25PAID
6701-04-19-0-000-007.000VERNON BROTHERS TRUST705 THOMAS LANE52019101-04-19-0-000-007.000202420247$369.90PAID
7801-04-19-0-000-008.000SEAY, RHONDA0801-04-19-0-000-008.000202420248$420.35PAID
8901-04-19-0-000-008.001BROWN, CLIFFORD1090 THOMAS LANE15868701-04-19-0-000-008.001202420249$982.00PAID
91001-04-19-0-000-008.002SEAY, RHONDA MARLAR01001-04-19-0-000-008.0022024202410$99.00PAID
IndexPin#DescriptionDescription1AccountParcelYearBilling YearPinTotal TaxBalance Due
618616186226-01-02-0-000-012.000KLEIN, WESLEY06527126-01-02-0-000-012.0002024202447465$76.70PAID
618626186326-01-02-0-000-013.000KLEIN, JOSEPH WESTLEY05728426-01-02-0-000-013.0002024202447466$42.95PAID
618636186426-01-11-0-000-001.000KLEIN, JIM04746726-01-11-0-000-001.0002024202447467$3.99PAID
618646186526-02-03-0-000-001.000WILSON, LARRY JOEL & ANGIE M010193126-02-03-0-000-001.0002024202447468$40.90PAID
618656186626-02-03-0-000-002.000BECK, TIMOTHY C04746926-02-03-0-000-002.0002024202447469$614.80PAID
618666186726-02-03-0-000-003.000BRIGHT, WILLIAM E04747026-02-03-0-000-003.0002024202447470$225.10PAID
618676186826-02-03-0-000-004.000PETERS, WAYNE04747126-02-03-0-000-004.0002024202447471$45.75PAID
618686186926-02-03-0-000-005.000DODD, BARRY EDWARD & DONNA JEAN0 NELSON BLUFF ROAD15130026-02-03-0-000-005.0002024202447472$118.62PAID
618696187026-02-03-0-000-005.001DODD, BARRY EDWARD & DONNA JEAN374 NELSON GAP ROAD4747326-02-03-0-000-005.0012024202447473$317.72PAID
618706187129-00-00-0-000-001.000TENNESSEE RIVER CHANNEL0 051799429-00-00-0-000-001.0002024202483584$0.00NONE